Voting rules as error-correcting codes

Procaccia, Ariel D.; Shah, Nisarg; Zick, Yair

doi:10.1016/j.artint.2015.10.003

Cited by 15 publications

(10 citation statements)

References 18 publications

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“…We believe that insights gained in the process of improving the competence of label ranking ensembles, may be valuable to a variety of fields that are concerned with rankings, including: combining voters' preferences in computational social choice [54], ensembles in machine learning, rank aggregation in information retrieval [55], group recommendations in recommender systems, phylogenetic profiling [56], and even error correction codes [57].…”

Section: Discussionmentioning

confidence: 99%

BoostLR: A Boosting-Based Learning Ensemble for Label Ranking Tasks

Dery

Shmueli

2020

IEEE Access

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Label ranking tasks are concerned with the problem of ranking a finite set of labels for each instance according to their relevance. Boosting is a well-known and reliable ensemble technique that was shown to often outperform other learning algorithms. While boosting algorithms were developed for a multitude of machine learning tasks, label ranking tasks were overlooked. Herein, we present a novel boosting algorithm, BoostLR, that was specifically designed for label ranking tasks. Similarly to other boosting algorithms, BoostLR, proceeds in rounds, where in each round, a single weak model is trained over a sampled set of instances. Instances that were identified as harder to predict in the current round, receive a higher (boosted) weight, and therefore also a higher probability to be included in the sample of the forthcoming round. Extensive evaluation of our proposed algorithm on 24 semi-synthetic and real-world label ranking datasets concludes that our algorithm significantly outperforms the current state-of-the-art label ranking methods.

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Section: Discussionmentioning

confidence: 99%

BoostLR: A Boosting-Based Learning Ensemble for Label Ranking Tasks

Dery

Shmueli

2020

IEEE Access

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“…Also, voting rules under adversarial noise model are studied. 39 Our work is distinguished from this literature by lack of the assumption of the common correct ranking for all voters; that is, individual rankings are subjective in our model rather than objective.…”

Section: Noisy Objective Preferencementioning

confidence: 99%

Group recommendation with noisy subjective preferences

Salehi‐Abari

Larson

2020

Computational Intelligence

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Funding information Natural Sciences and Engineering Research Council of Canada Social choice theory provides a principled framework for the aggregation of individuals' preferences in support of group decision-making and recommendation. Much of this work, however, either assumes that individuals' subjective preferences (and thus, their votes) are correctly specified by the individuals themselves, or alternatively that the votes of individuals are noisy estimates of some underlying ground truth over rankings of alternatives. We argue that neither model appropriately addresses some of the issues which arise in the context of group-recommendation domains where individuals have subjective preferences but for some reason (eg, the high cognitive burden, concerns about privacy, etc.) may instead vote using a noisy estimate of their subjective preference rankings. In this paper, we propose a general probabilistic framework for modeling noisy subjective preferences, and explore the accuracy and reliability of four well-studied voting rules under various noise models. Our results demonstrate that there is no single reliable method amongst the examined methods. Specifically, we observe the change in noise distribution can flip one method from being the most reliable to the least.

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“…Another related topic is making a collective decision from noisy observations on voters' opinions, which is considered in the area of social choice and ranking system. Some previous works [36], [37] related the estimation of the majority vote with an error correction process.…”

Section: Introductionmentioning

confidence: 99%

Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks

Sohn,

Han,

Choi

et al. 2019

Preprint

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Recent advances in large-scale distributed learning algorithms have enabled communication-efficient training via SIGNSGD. Unfortunately, a major issue continues to plague distributed learning: namely, Byzantine failures may incur serious degradation in learning accuracy. This paper proposes ELECTION CODING, a coding-theoretic framework to guarantee Byzantine-robustness for SIGNSGD WITH MAJORITY VOTE, which uses minimum worker-master communication in both directions. The suggested framework explores new information-theoretic limits of finding the majority opinion when some workers could be malicious, and paves the road to implement robust and efficient distributed learning algorithms. Under this framework, we construct two types of explicit codes, random Bernoulli codes and deterministic algebraic codes, that can tolerate Byzantine attacks with a controlled amount of computational redundancy. For the Bernoulli codes, we provide upper bounds on the error probability in estimating the majority opinion, which give useful insights into code design for tolerating Byzantine attacks. As for deterministic codes, we construct an explicit code which perfectly tolerates Byzantines, and provide tight upper/lower bounds on the minimum required computational redundancy. Finally, the Byzantine-tolerance of the suggested coding schemes is confirmed by deep learning experiments on Amazon EC2 using Python with MPI4py package.

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Voting rules as error-correcting codes

Cited by 15 publications

References 18 publications

BoostLR: A Boosting-Based Learning Ensemble for Label Ranking Tasks

BoostLR: A Boosting-Based Learning Ensemble for Label Ranking Tasks

Group recommendation with noisy subjective preferences

Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks

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